Is the deconvolution layer the same as a convolutional layer?
To address this problem, aiming at complex data, we propose a new CapsNet architecture called Fractionally-Strided Convolutional Capsule Network (FSC-CapsNet). We modify both network structures of the encoder and decoder of CapsNet. For the purpose of extracting better features, we increase the ...
问在as_strided进行优化之前进行整形EN本文介绍了如何利用MySQL数据库对某电商平台进行性能优化，通过合理...
In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. Steve Wang 2019/05/28 2.1K0 通俗易懂!使用Excel和TF实现Transformer! 编程算法javascript 中文词表:[机、器、学、习] 英文词表[...
Convolutional Neural Networks. Contribute to pjreddie/darknet development by creating an account on GitHub.
Strided-CNNPool-CNNIn this paper, a deep convolutional neural network (CNN) is proposed for accurate segmentation of retinal blood vessels. This method plays a significant role in observing many eye diseases. A strided-CNN model is proposed for accurate segmentation of retinal vessels, especially...
In this case, only the inference of convolutional neural networks is discussed. As known that both pooling layer and strided convolution can be used to summarize the data. So, the proposed technique aims to replace only max pooling layers by a strided convolution layers using the same filter ...
We proposed a combination of atrous and fractionally strided convolutional neural network (CAFN), which is merely constituted by two components: an atrous convolutional neural network as the front-end for 2D features extraction which utilizes dilated kernels to deliver larger receptive fields and to ...
FFTARMv8Parallel algorithmConvolutional Neural Networks (CNNs) have been widely adopted in many kinds of artificial intelligence applications. Most of the computational overhead of CNNs is spent on convolutions. An effective approach to reducing the overhead is transforming convolutions in the time ...
Convolutional neural networkMedical image classificationSelf-supervised denoisingLow-dose CTLung nodule classification based on low-dose computed tomography(LDCT)images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based ...